This white paper provides an overview of how different NDT techniques can be modeled and simulated, highlighting the need for modern CAE tools that enable an efficient exploration of all variables involved.

By DE Editors

September 18, 2019

Dear DE Reader,

There is an amazing transformation taking place across the entire global economy as businesses race to develop and deploy artificial intelligence (AI) and data analytics (DA) solutions to reduce time to market, develop and deliver innovative products and services, and explore vast amounts of data to develop actionable business strategies to gain competitive advantage. Recent advances in GPU acceleration of AI and DA development frameworks, particularly with the wide availability of Tensor Cores in Quadro RTX graphics boards, reset hardware performance and Data Science productivity expectations, by delivering turnkey solutions that address this market.

Earlier this year NVIDIA announced a new specification called the NVIDIA-Powered Data Science Workstation (DSW). This platform combines the latest generation of NVIDIA GPU technology with a software stack for Data Science professionals. As NVIDIA’s master channel partner across NALA and EMEAI, PNY, and their ecosystem of partners offering scalable NVIDIA-Powered DSW systems, is our Editor’s Pick of the Week.

Why pick an NVIDIA-Powered DSW instead of the standard engineering workstation? Here are two reasons. The first is fundamental hardware configuration and software stacks that fundamentally differentiate DSW’s from traditional engineering CAD or CAE workstations. The second is a dramatically different software stack, and constellation of ISV applications, required to perform data science and data analytics, without requiring high-cost Data Science professionals to become systems integrators by delivering a turnkey solution that just works out of the box.

The NVIDIA-Powered DSW specification includes a software stack that starts with Ubuntu Linux 18.04, nicknamed Bionic Beaver. The bundled software for data analysis includes a set of software libraries based on the NVIDIA CUDA-X AI framework for AI research. Also included are RAPIDS, TensorFlow, PyTorch and Caffe open source libraries, as well as several NVIDIA-written acceleration libraries for machine learning, artificial intelligence and deep learning.

Custom workstations for data science workers address the unique needs of AI, deep learning and data analytics. Many tasks, particularly data preparation and DNN (Deep Neural Network) training are repetitive in nature, performing the same steps millions or billions of times. Such repetitive processes are better suited for parallel processing on GPUs than CPUs. NVIDIA’s new Quadro RTX (Turing) GPUs include Tensor Cores, specialized processors that do matrix arithmetic – which are foundational to AI and data analytics – calculations orders of magnitude faster than CPUs.

As NVIDIA’s master channel partner, PNY has amassed the years of experience required to turn NVIDIA’s demanding reference designs into products, in conjunction with select PNY hardware partners. The Aeronautics and Astronautics department at the Massachusetts Institute of Technology was a pre-release user of the NVIDIA-Powered DSW specification. “The NVIDIA-powered data science workstation provides significant capabilities for training deep neural networks for robot perception. With it, the MIT FAST Labs’ ability to train drones to see depth and avoid collisions from a single camera was significantly accelerated because we could process larger batch sizes,” says Sertac Karaman, an associate professor in the department.

There is so much more we could say about this exciting new approach to AI, Data Science, and Big Data Analytics. Check out our Product Brief for information about some of the vendors who are creating DSW’s using PNY-sourced NVIDIA technology.

Thanks for reading, we’ll be back next week with another Editor’s Pick of the Week.

The detailed design process is complex and requires time, effort, and expertise to tackle efficiently. Visualization and simulation have become key to many organizations, but until now both required too much time to truly influence the early stages of design.